- Recap Pilot 1
- Recap Study 1: bad characters
- Pilot 2
- Study 2: good characters
- Study 3: both good and bad
- Study 4: good characters
- Study 5: both good and bad (between subjects)
16 May 2022
Overview
Pilot Study 1 (Recap)
Pilot Study 1
Developing materials
6 descriptions:
- 4 morally relevant (diagnostic)
- 2 neutral (non-diagnostic)
MTurk sample, N = 212, (female = 80, male = 128, non-binary = 1, prefer not to say = 1, Mage = 36.6, SD = 10.3).
Materials: Moral Descriptions
Imagine a person named Sam. Throughout their life they have been known to be cruel, act unfairly, and to betray their own group.
Imagine a person named Robin. Throughout their life they have been known to physically hurt others, treat some people differently to others, and show lack of loyalty.
Imagine a person named Francis. Throughout their life they have been known to violate the standards of purity and decency, show lack of respect for authority, and treat people unequally.
Imagine a person named Alex. Throughout their life they have been known to cause others to suffer emotionally, to deny others their rights, and to cause chaos or disorder.
(adapted from Grizzard et al., 2020)
Materials: Non-Diagnostic
Imagine a person named Jackie. They have red hair, play tennis four times a month, and have one older sibling and one younger sibling.
Imagine a person named Charlie. They are left-handed, drink tea in the morning, and have two older siblings and one younger sibling.
Measures:
- DV1: Moral perceptions scale (Walker et al., 2021):
- 7-point Likert
- 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic
- Cronbach’s alpha = 0.93
- DV2: Single item of moral perception (Walker et al., 2021).
- 0-100 slider scale
- Very Immoral-Very Moral
Results (Single Item): Scenario
Results (Single Item): Condition
A paired samples t-test revealed a significant difference in moral Perception between the Diagnostic Condition, (M = 56.54, SD = 28.56), and the Non-Diagnostic condition, (M =72.97, SD = 13.89), t(211) = -8.735, p < .001, d = 0.6.
Results (Single Item): Condition
Results (Scale): Scenario
Results (Scale): Condition
A paired samples t-test revealed a significant difference in moral Perception between the Diagnostic Condition, (M = 4.4, SD = 175), and the Non-Diagnostic condition, (M = 5.39, SD = .98), t(211) = -8.655, p < .001, d = 0.59.
Results (Scale): Condition
Combined (Scale - Condition & Scenario)
## ## Error: ResponseId ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 211 2215 10.5 ## ## Error: ResponseId:condition ## Df Sum Sq Mean Sq F value Pr(>F) ## condition 1 274.6 274.56 74.92 1.27e-15 *** ## Residuals 211 773.3 3.66 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Error: ResponseId:scenario_abb ## Df Sum Sq Mean Sq F value Pr(>F) ## scenario_abb 4 2.3 0.5746 1.487 0.204 ## Residuals 844 326.2 0.3865
Study 1 (Recap)
Study 1
Study 1: Design
- Within subjects design
- IV: Diagnostic information (present/absent)
- 4 descriptions
- Randomly assigned to contain diagnostic information vs not
- Imagine a person named Sam. Throughout their life they have been known to be cruel, act unfairly, and to betray their own group.
- Imagine a person named Sam. Throughout their life they have been known to be cruel, act unfairly, and to betray their own group. They are left-handed, drink tea in the morning, and have two older siblings and one younger sibling.
Materials: Moral Descriptions
Imagine a person named Sam. Throughout their life they have been known to be cruel, act unfairly, and to betray their own group.
Imagine a person named Robin. Throughout their life they have been known to physically hurt others, treat some people differently to others, and show lack of loyalty.
Imagine a person named Francis. Throughout their life they have been known to violate the standards of purity and decency, show lack of respect for authority, and treat people unequally.
Imagine a person named Alex. Throughout their life they have been known to cause others to suffer emotionally, to deny others their rights, and to cause chaos or disorder.
Materials: Non-Diagnostic
They have red hair, play tennis four times a month, and have one older sibling and one younger sibling.
They are left-handed, drink tea in the morning, and have two older siblings and one younger sibling.
Study 1
- DV1: Moral perceptions scale (Walker et al., 2021):
- 7-point Likert
- 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic
- Cronbach’s alpha = 0.83
- DV2: Single item of moral perception (Walker et al., 2021).
- 0-100 slider scale
- Very Immoral-Very Moral
UL Students with a total sample of N = 801, (female = 496, male = 283, non-binary/other = 17, prefer not to say 3, Mage = 26.2, SD = 10.2).
Results: Single Item Measure
## numDF denDF F-value p-value ## (Intercept) 1 2396 2180.9685 <.0001 ## condition 1 2396 47.1470 <.0001 ## scenario 3 2396 156.7080 <.0001 ## condition:scenario 3 2396 2.0149 0.1098
Differences by Condition (single item)
Differences by Scenario (single item)
Results: Scale
## numDF denDF F-value p-value ## (Intercept) 1 2396 10208.623 <.0001 ## condition 1 2396 53.278 <.0001 ## scenario 3 2396 304.146 <.0001 ## condition:scenario 3 2396 0.997 0.3933
Differences by Condition (scale)
Differences by Scenario (scale)
Pilot Study 2
Pilot Study 2
Developing materials
6 descriptions:
- 4 morally relevant (diagnostic)
- 2 neutral (non-diagnostic)
MTurk sample, N = 215, (female = 63, male = 152, non-binary = 0, prefer not to say = 0, Mage = 36.6, SD = 9.6).
Materials: Moral Descriptions
Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others.
Imagine a person named Robin. Throughout their life they have been known to show compassion and empathy for others, act with a sense of fairness and justice, and, never to break their word.
Imagine a person named Francis. Throughout their life they have been known to uphold the standards of purity and decency, show respect for authority, and to always act honestly and fairly.
Imagine a person named Alex. Throughout their life they have been known to protect and provide shelter to the weak and vulnerable, uphold the rights of others, and show respect for authority.
Materials: Non-Diagnostic
Imagine a person named Jackie. They have dark hair, go for a jog twice a week, and their favourite colour is blue.
Imagine a person named Charlie. They have blue eyes, drink coffee in the morning, and their favourite colour is green.
Measures:
- DV1: Moral perceptions scale (Walker et al., 2021):
- 7-point Likert
- 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic
- Cronbach’s alpha = 0.84
- DV2: Single item of moral perception (Walker et al., 2021).
- 0-100 slider scale
- Very Immoral-Very Moral
Results (Single Item): Scenario
Results (Single Item): Condition
A paired samples t-test revealed a significant difference in moral Perception between the Diagnostic Condition, (M = 56.54, SD = 28.56), and the Non-Diagnostic condition, (M =72.97, SD = 13.89), t(211) = -8.735, p < .001, d = 0.6.
Results (Single Item): Condition
Results (Scale): Scenario
Results (Scale): Condition
A paired samples t-test revealed a significant difference in moral Perception between the Diagnostic Condition, (M = 4.4, SD = 175), and the Non-Diagnostic condition, (M = 5.39, SD = .98), t(211) = -8.655, p < .001, d = 0.59.
Results (Scale): Condition
Combined (Scale - Condition & Scenario)
## ## Error: ResponseId ## Df Sum Sq Mean Sq F value Pr(>F) ## Residuals 214 849.8 3.971 ## ## Error: ResponseId:condition ## Df Sum Sq Mean Sq F value Pr(>F) ## condition 1 41.89 41.89 42.33 5.38e-10 *** ## Residuals 214 211.78 0.99 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Error: ResponseId:scenario_abb ## Df Sum Sq Mean Sq F value Pr(>F) ## scenario_abb 4 1.94 0.4850 2.98 0.0185 * ## Residuals 856 139.33 0.1628 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Study 2
Study 2
Study 2: Design
- Within subjects design
- IV: Diagnostic information (present/absent)
- 4 descriptions
- Randomly assigned to contain diagnostic information vs not
- Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others.
- Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others. They have dark hair, go for a jog twice a week, and their favourite colour is blue
Materials: Moral Descriptions
Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others.
Imagine a person named Robin. Throughout their life they have been known to show compassion and empathy for others, act with a sense of fairness and justice, and, never to break their word.
Imagine a person named Francis. Throughout their life they have been known to uphold the standards of purity and decency, show respect for authority, and to always act honestly and fairly.
Imagine a person named Alex. Throughout their life they have been known to protect and provide shelter to the weak and vulnerable, uphold the rights of others, and show respect for authority.
Materials: Non-Diagnostic
They have dark hair, go for a jog twice a week, and their favourite colour is blue.
They have blue eyes, drink coffee in the morning, and their favourite colour is green.
Study 2 Measures (Same as previous)
DV1: Moral perceptions scale, 7-point Likert; 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic; Cronbach’s alpha = 0.85
DV2: Single item of moral perception; 0-100 slider scale; Very Immoral-Very Moral
Total sample of N = 820, (female = 466, male = 337, non-binary/other = 15, prefer not to say 4, Mage = 29, SD = 10.9).
The majority of participants were from the student body: n = 533, (female = 370, male = 147, non-binary/other = 14, prefer not to say 3, Mage = 25.5, SD = 9.6).
In order to reach our pre-registered target sample size we recruited additional participants from MTurk: n = 287, (female = 96, male = 190, non-binary/other = 1, prefer not to say 1, Mage = 35.7, SD = 10.1).
Results: Single Item Measure
## numDF denDF F-value p-value ## (Intercept) 1 2453 35258.06 <.0001 ## condition 1 2453 0.52 0.4691 ## scenario 3 2453 24.46 <.0001 ## condition:scenario 3 2453 0.92 0.4282
Differences by Condition (single item)
Differences by Scenario (single item)
Results: Scale
## numDF denDF F-value p-value ## (Intercept) 1 2453 36236.50 <.0001 ## condition 1 2453 0.02 0.8883 ## scenario 3 2453 54.49 <.0001 ## condition:scenario 3 2453 0.44 0.7243
Differences by Condition (scale)
Differences by Scenario (scale)
Study 3
## Warning in rm(x, y, y1, y2, y3, z): object 'x' not found
Study 3
- Both good and bad characters
- Within subjects factorial design
- IV1: Diagnostic information (present/absent)
- IV2: Valence of description (good/bad)
- 4 descriptions (2 good, 2 bad)
- Good: Sam & Robin
- Bad: Alex & Francis
- Randomly assigned to contain diagnostic information vs not
Study 3 (descriptions)
Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others. (CFL)
Imagine a person named Robin. Throughout their life they have been known to show compassion and empathy for others, act with a sense of fairness and justice, and, never to break their word. (CFL)
Imagine a person named Alex. Throughout their life they have been known to be cruel, act unfairly, and to betray their own group. (CFL)
Imagine a person named Francis. Throughout their life they have been known to physically hurt others, treat some people differently to others, and show lack of loyalty. (CFL)
Study 3 (non-diagnostic)
- They have red hair, play tennis four times a month, and have one older sibling and one younger sibling.
- They are left-handed, drink tea in the morning, and have two older siblings and one younger sibling.
Study 3 Measures (Same as previous)
DV1: Moral perceptions scale, 7-point Likert; 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic; Cronbach’s alpha = 0.94
DV2: Single item of moral perception; 0-100 slider scale; Very Immoral-Very Moral
MTurk sample of N = 874, (female = 320, male = 550, non-binary/other = 4, prefer not to say 2, Mage = 36.4, SD = 10.7).
Study 3: Results - Interaction (Single Item)
## numDF denDF F-value p-value ## (Intercept) 1 2619 16705.051 <.0001 ## condition 1 2619 0.005 0.9413 ## valence 1 2619 1377.694 <.0001 ## condition:valence 1 2619 2.684 0.1015
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 0.03610554 8.493419e-01 1.340084e-06 ## 3 valence 1 873 512.45355672 1.242389e-89 * 2.542259e-01 ## 4 condition:valence 1 873 16.61251479 5.002870e-05 * 6.637092e-04
Condition vs Valence (single item)
Differences between Scenarios (single item)
Study 3: Results - Interaction (Scale)
## numDF denDF F-value p-value ## (Intercept) 1 2619 21396.991 <.0001 ## condition 1 2619 0.001 0.9750 ## valence 1 2619 1327.693 <.0001 ## condition:valence 1 2619 1.430 0.2318
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 0.00669487 9.348069e-01 2.336129e-07 ## 3 valence 1 873 494.20162281 4.103959e-87 * 2.393100e-01 ## 4 condition:valence 1 873 8.60784490 3.434814e-03 * 3.388382e-04
Condition vs Valence (Scale)
Differences between Scenarios (Scale)
Study 3 Bad (single item)
## numDF denDF F-value p-value ## (Intercept) 1 873 2273.1467 <.0001 ## condition 1 873 6.9807 0.0084
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 6.98073 0.00838666 * 0.0004408043
## $diagnostic ## mean sd min max len ## 1 4.019451 2.09108 1 7 874 ## ## $`non-diagnostic` ## mean sd min max len ## 1 4.08095 2.013616 1 7 874
Study 3 Bad (scale)
## numDF denDF F-value p-value ## (Intercept) 1 873 3603.748 <.0001 ## condition 1 873 3.513 0.0612
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 3.513295 0.06121202 0.0002246037
## $diagnostic ## mean sd min max len ## 1 4.019451 2.09108 1 7 874 ## ## $`non-diagnostic` ## mean sd min max len ## 1 4.08095 2.013616 1 7 874
Study 3 Good (single item)
## numDF denDF F-value p-value ## (Intercept) 1 873 28302.926 <.0001 ## condition 1 873 12.042 5e-04
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 12.0419 0.0005456622 * 0.001700532
## $diagnostic ## mean sd min max len ## 1 5.905034 1.061668 1.75 7 874 ## ## $`non-diagnostic` ## mean sd min max len ## 1 5.846682 1.025843 1.5 7 874
Study 3 Good (scale)
## numDF denDF F-value p-value ## (Intercept) 1 873 30756.813 <.0001 ## condition 1 873 6.848 0.009
## Warning: Converting "ResponseId" to factor for ANOVA.
## $ANOVA ## Effect DFn DFd F p p<.05 ges ## 2 condition 1 873 6.847735 0.00902883 * 0.000781429
## $diagnostic ## mean sd min max len ## 1 5.905034 1.061668 1.75 7 874 ## ## $`non-diagnostic` ## mean sd min max len ## 1 5.846682 1.025843 1.5 7 874
Study 4
## [1] 507
Study 4
- Replication of Study 2
- Same design/materials as Study 2
- Good characters Only
Study 4: Design
- Within subjects design
- IV: Diagnostic information (present/absent)
- 4 descriptions
- Randomly assigned to contain diagnostic information vs not
- Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others.
- Imagine a person named Sam. Throughout their life they have been known to always help and care for others, treat everyone fairly and equally, and show a strong sense of loyalty to others. They have dark hair, go for a jog twice a week, and their favourite colour is blue
Study 4 Measures (Same as previous)
DV1: Moral perceptions scale, 7-point Likert; 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic; Cronbach’s alpha = 0.81
DV2: Single item of moral perception; 0-100 slider scale; Very Immoral-Very Moral
MTurk sample of N = 856, (female = 347, male = 507, non-binary/other = 2, prefer not to say 1, Mage = 37.1, SD = 11).
Results: Single Item Measure
## numDF denDF F-value p-value ## (Intercept) 1 2561 34256.32 <.0001 ## condition 1 2561 4.53 0.0334 ## scenario 3 2561 3.29 0.0199 ## condition:scenario 3 2561 2.73 0.0426
Differences by Condition (single item)
Differences by Scenario (single item)
Results: Scale
## numDF denDF F-value p-value ## (Intercept) 1 2561 41094.11 <.0001 ## condition 1 2561 2.21 0.1372 ## scenario 3 2561 3.56 0.0137 ## condition:scenario 3 2561 0.87 0.4542
Differences by Condition (scale)
Differences by Scenario (scale)
Study 5
Study 5
- Same materials and measures as Study 3 but with a Between Subjects Design
- Participants read 1 scenario
- 2 IVs:
- valence (good/bad)
- condition (diagnostic/non-diagnostic)
Study 5 Measures (Same as previous)
DV1: Moral perceptions scale, 7-point Likert; 4 items: Bad-Good, Moral-Immoral, Violent-Peaceful, Merciless-Empathetic; Cronbach’s alpha = 0.97
DV2: Single item of moral perception; 0-100 slider scale; Very Immoral-Very Moral
MTurk sample of N = 1750, (female = 858, male = 879, non-binary/other = 12, prefer not to say 8, Mage = 37.8, SD = 15.8)..
Study 5: Results - Interaction (Single Item)
## numDF denDF F-value p-value ## (Intercept) 1 1746 10616.659 <.0001 ## condition 1 1746 0.206 0.6499 ## valence 1 1746 977.372 <.0001 ## condition:valence 1 1746 9.633 0.0019
Condition vs Valence (single item)
Differences between Scenarios (single item)
Study 3: Results - Interaction (Scale)
## numDF denDF F-value p-value ## (Intercept) 1 1746 16699.127 <.0001 ## condition 1 1746 0.014 0.9066 ## valence 1 1746 1004.460 <.0001 ## condition:valence 1 1746 5.451 0.0197
Condition vs Valence (Scale)
Differences between Scenarios (Scale)
Study 5 Bad (single item)
## numDF denDF F-value p-value ## (Intercept) 1 858 1459.2939 <.0001 ## condition 1 858 2.1923 0.1391
## $diagnostic ## mean sd min max len ## 1 3.631675 2.212024 1 7 412 ## ## $`non-diagnostic` ## mean sd min max len ## 1 3.80971 2.031748 1 7 448
Study 5 Bad (scale)
## numDF denDF F-value p-value ## (Intercept) 1 858 2654.2173 <.0001 ## condition 1 858 1.5136 0.2189
## $diagnostic ## mean sd min max len ## 1 3.631675 2.212024 1 7 412 ## ## $`non-diagnostic` ## mean sd min max len ## 1 3.80971 2.031748 1 7 448
Study 5 Good (single item)
## numDF denDF F-value p-value ## (Intercept) 1 888 28872.505 <.0001 ## condition 1 888 19.177 <.0001
## $diagnostic ## mean sd min max len ## 1 6.252921 0.8393273 1 7 428 ## ## $`non-diagnostic` ## mean sd min max len ## 1 6.07197 0.8701416 2.75 7 462
Study 3 Good (scale)
## numDF denDF F-value p-value ## (Intercept) 1 888 46132.44 <.0001 ## condition 1 888 9.94 0.0017
## $diagnostic ## mean sd min max len ## 1 6.252921 0.8393273 1 7 428 ## ## $`non-diagnostic` ## mean sd min max len ## 1 6.07197 0.8701416 2.75 7 462
Testing individual scenarios
Sam (Good)
## $diagnostic ## mean sd min max len ## 1 87.19718 13.37093 50 100 213 ## ## $`non-diagnostic` ## mean sd min max len ## 1 81.98667 16.90348 0 100 225
## ## Welch Two Sample t-test ## ## data: x$M1 by x$condition ## t = 3.5877, df = 422.8, p-value = 0.0003726 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## 2.355838 8.065195 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 87.19718 81.98667
## $diagnostic ## mean sd min max len ## 1 6.228873 0.8492642 1 7 213 ## ## $`non-diagnostic` ## mean sd min max len ## 1 6.031111 0.8836491 2.75 7 225
## ## Welch Two Sample t-test ## ## data: x$R_tot by x$condition ## t = 2.3883, df = 435.9, p-value = 0.01735 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## 0.03501707 0.36050719 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 6.228873 6.031111
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$M1 by x$condition ## W = 28388, p-value = 0.000766 ## alternative hypothesis: true location shift is not equal to 0
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$R_tot by x$condition ## W = 27368, p-value = 0.009167 ## alternative hypothesis: true location shift is not equal to 0
Robin (Good)
## $diagnostic ## mean sd min max len ## 1 87.02791 14.13814 0 100 215 ## ## $`non-diagnostic` ## mean sd min max len ## 1 83.44726 14.8576 22 100 237
## ## Welch Two Sample t-test ## ## data: x$M1 by x$condition ## t = 2.6247, df = 448.96, p-value = 0.008969 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## 0.8995724 6.2617268 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 87.02791 83.44726
## $diagnostic ## mean sd min max len ## 1 6.276744 0.8306588 2 7 215 ## ## $`non-diagnostic` ## mean sd min max len ## 1 6.110759 0.8571856 4 7 237
## ## Welch Two Sample t-test ## ## data: x$R_tot by x$condition ## t = 2.0896, df = 448.03, p-value = 0.03721 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## 0.009877705 0.322091680 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 6.276744 6.110759
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$M1 by x$condition ## W = 29142, p-value = 0.007704 ## alternative hypothesis: true location shift is not equal to 0
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$R_tot by x$condition ## W = 28622, p-value = 0.02105 ## alternative hypothesis: true location shift is not equal to 0
Francis (Bad)
## $diagnostic ## mean sd min max len ## 1 42.02791 35.9883 0 100 215 ## ## $`non-diagnostic` ## mean sd min max len ## 1 46.97235 34.05101 0 100 217
## ## Welch Two Sample t-test ## ## data: x$M1 by x$condition ## t = -1.4665, df = 428.22, p-value = 0.1432 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## -11.571240 1.682353 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 42.02791 46.97235
## $diagnostic ## mean sd min max len ## 1 3.598837 2.272306 1 7 215 ## ## $`non-diagnostic` ## mean sd min max len ## 1 3.842166 2.046233 1 7 217
## ## Welch Two Sample t-test ## ## data: x$R_tot by x$condition ## t = -1.1692, df = 424.52, p-value = 0.243 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## -0.6523885 0.1657312 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 3.598837 3.842166
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$M1 by x$condition ## W = 21254, p-value = 0.1098 ## alternative hypothesis: true location shift is not equal to 0
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$R_tot by x$condition ## W = 21624, p-value = 0.188 ## alternative hypothesis: true location shift is not equal to 0
Alex (Bad)
## $diagnostic ## mean sd min max len ## 1 44.79695 35.12557 0 100 197 ## ## $`non-diagnostic` ## mean sd min max len ## 1 46.74892 33.7386 0 100 231
## ## Welch Two Sample t-test ## ## data: x$M1 by x$condition ## t = -0.5835, df = 409.65, p-value = 0.5599 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## -8.527947 4.624020 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 44.79695 46.74892
## $diagnostic ## mean sd min max len ## 1 3.667513 2.14951 1 7 197 ## ## $`non-diagnostic` ## mean sd min max len ## 1 3.779221 2.022015 1 7 231
## ## Welch Two Sample t-test ## ## data: x$R_tot by x$condition ## t = -0.55066, df = 406.27, p-value = 0.5822 ## alternative hypothesis: true difference in means between group diagnostic and group non-diagnostic is not equal to 0 ## 95 percent confidence interval: ## -0.5104988 0.2870827 ## sample estimates: ## mean in group diagnostic mean in group non-diagnostic ## 3.667513 3.779221
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$M1 by x$condition ## W = 21867, p-value = 0.487 ## alternative hypothesis: true location shift is not equal to 0
## ## Wilcoxon rank sum test with continuity correction ## ## data: x$R_tot by x$condition ## W = 21906, p-value = 0.5058 ## alternative hypothesis: true location shift is not equal to 0
Plotting Scenario by condition
Bad (single item)
Bad (scale)
Good (single item)
Good (scale)
Summary
Summary
Summary
Summary
There is a convincing effect for bad characters when they are contrasted against other bad characters. But this effect shrinks (or disappears) when they are contrasted against good characters.
There is a convincing effect for good characters when there is no contrast, or when they can be contrasted against bad characters; but this disappears (or shrinks) when other good characters are the only contrast.